In application to electric vehicles, it is particularly desirable to indicate to the vehicle operator the useful remaining energy of the motive battery system and/or range available therefrom in a fashion much analogous to a fuel gage or range gage in a conventional internal combustion powered vehicle. This information will of course inform the operator when the capacity of the motive battery system is nearing depletion thereby necessitating a recharge in order to avoid operator inconvenience and/or irreversible battery damage. A further need exists for such information at the system level so that efficient recharging can be performed based upon the motive battery system present state-of-charge and capacity.
Prior art ampere-hour integration techniques have been used to indicate battery state-of-charge but fall short of accurately predicting state-of-charge in vehicle specific applications which are characterized by dynamic battery capacities related to varying discharge current magnitude and rates as well as variable temperature operating conditions. Prior art battery terminal voltage techniques have also been utilized to indicate battery state-of-charge but are expensive due to the precision voltage measurements required and suffer from implementation problems into a dynamic system since they typically require open circuit, near zero current, voltage measurements thereby requiring operative interruption. Additionally, after termination or interruption of current flow for terminal voltage measurement, the polarization voltage decay time constant would be on a much greater order of magnitude than any practical period through which a current interruption could be tolerated during any dynamic electric vehicle operation or expedient recharging cycle, thereby resulting in inaccuracies attributable thereto.
Neural network architecture has been proposed for determining battery state-of-charge for stand alone systems using a battery model or actual battery response data. The suggested training methods of these techniques ignore the wide range of discharge and charge currents, relatively high occurrence frequency of high amplitude transient currents, and relatively frequent alternation between discharge and charge cycling experienced in certain applications, for example, in motive battery packs for providing power to an electrically driven vehicle experiencing: very short, dynamic charge cycles due to regenerative braking, relatively quick bulk recharge cycles to less than full state-of-charge, and relatively long full recharge cycles to 100% capacity. Instead, such training methods suggest concentration upon a much narrower input data set comprising a relatively limited range of discharge state space. The results of such techniques tend to be prone to predicted state-of-charge inaccuracies exceeding 20% at extreme actual state-of-charge conditions.